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Found 7 papers, 1 papers with code
DREQ: Document Re-Ranking Using Entity-based Query Understanding
While entity-oriented neural IR models have advanced significantly, they often overlook a key nuance: the varying degrees of influence individual entities within a document have on its overall relevance.
TREC iKAT 2023: The Interactive Knowledge Assistance Track Overview
Conversational Information Seeking has evolved rapidly in the last few years with the development of Large Language Models providing the basis for interpreting and responding in a naturalistic manner to user requests.
Adaptive Latent Entity Expansion for Document Retrieval
First, we demonstrate that applying a strong neural re-ranker before sparse or dense PRF can improve the retrieval effectiveness by 5-8%.
GRM: Generative Relevance Modeling Using Relevance-Aware Sample Estimation for Document Retrieval
Recent studies show that Generative Relevance Feedback (GRF), using text generated by Large Language Models (LLMs), can enhance the effectiveness of query expansion.
Generative and Pseudo-Relevant Feedback for Sparse, Dense and Learned Sparse Retrieval
Pseudo-relevance feedback (PRF) is a classical approach to address lexical mismatch by enriching the query using first-pass retrieval.
Generative Relevance Feedback with Large Language Models
Current query expansion models use pseudo-relevance feedback to improve first-pass retrieval effectiveness; however, this fails when the initial results are not relevant.
Report on the First HIPstIR Workshop on the Future of Information Retrieval
The vision of HIPstIR is that early stage information retrieval (IR) researchers get together to develop a future for non-mainstream ideas and research agendas in IR.
